This application relates to a fault diagnosis system.
Fault diagnosis of a gas turbine engine consists of two discrete stages, fault detection and fault isolation. Typically, these systems identify faults, and provide a maintenance worker with a likely location for a fault based upon a particular set of sensed system conditions.
A fault detection algorithm is responsible for monitoring engine sensors for sudden changes, which would indicate some amount of damage may have been sustained by the engine. The magnitude of the change in each sensor is passed on to a fault isolator, which determines the likely cause of the measured shift in the sensor readings. The fault isolator then directs a ground technician to the likely location of the damage. One type of fault isolation for a gas turbine engine is model-based. In such systems, a number of sensed conditions are developed, and a corresponding fault is predicted. These systems are often not based upon real world cases, but rather on computer modeling. In such systems, a vector of expected measurement shift magnitudes is created for each possible fault type. The measurement shift magnitudes represent the change in operating parameters that should correspond to that particular fault, and should be seen by the relevant sensors. These sets of measurement shift magnitudes are referred to as “fault signatures.” A set of example faults and corresponding measured changes are provided in FIG. 1. Each value listed in FIG. 1 represents the sensed shift from a normal operating condition. Of course, these numbers are all simple examples.
In order to determine a cause of a sudden change in engine performance, the fault signatures of FIG. 1 are each projected onto a vector of measurement shift magnitudes. A measurement error is then calculated for each fault as the norm of the difference of the vectors, normalized by the measurement variance of each sensor. Mathematically, a measurement error can be calculated utilizing the following expression:
                    ∑                  i          =          1                m            ⁢                        (                                                    Measurement                i                            -                              FaultSignature                i                                                    SensorNoise              i                                )                2              ,where Measurementi is the measurement for sensor i from the engine to be isolated, FaultSignaturei is a fault signature from the engine model for sensor i, SensorNoisei is the standard deviation of the measurements for sensor i, and m is the total number of sensors monitored.
In current practice, a measurement error is used as a measurement of goodness-of-fit. A large measurement error usually represents a poorly isolated fault. Once an error is calculated for each fault signature, the errors are ranked from smallest to greatest and the smallest error is selected as the most likely cause of the shift. However, this is an imperfect measure of appropriate confidence in a predicted fault, because it does not take into account other potential faults that may be close to the predicted fault. In other words, this method will always result in the identification of some fault as the likely cause, but it is often difficult to judge the accuracy of the system (how much confidence should be placed in the calculated results) using the measurement error alone.
In typical systems, there may be as many as 30 faults that must be distinguished by the eight sensors. In such systems, it is inevitable that some fault signatures appear very similar to others. These ambiguities reduce the overall accuracy of the system in certain regions of the input space.
Although a model-based approach to isolation, which is popular in the gas-turbine field, is discussed above, an alternative approach would be to implement an empirical system. In an empirical fault isolator, a generic computer or mathematical algorithm is presented with a large set of example fault cases, often collected from real world operation. From this data, the algorithm learns how to distinguish possible faults without any prior knowledge of the true engine model. Like the model-based method discussed above, some empirical approaches also generate some figure of isolation error. However, as in the model-based case, these approaches will always produce a prediction regardless of how ridiculous the input measurement vector. Therefore, the measurement or confidence measure output by the algorithm represents the confidence of the algorithm and its limited knowledge, not confidence in the algorithm itself.
When attempting to improve the current isolation methods, or to identify an alternative approach, the lack of a single unifying confidence measure across all of the different possible isolation methods complicates comparisons between isolation approaches. This problem is made worse when the results of several different isolation algorithms are consulted before coming to a final decision. As an example, it has been proposed recently to combine model-based with empirical fault isolation systems. Developing a confidence measure for these combined systems will be complicated based upon the prior art because the error and confidence measures provided inherently in each approach are each different and cannot easily be combined in a meaningful fashion.